Ushas AK | Artificial Intelligence | Best Researcher Award

Published on by Global Academic Awards

Best Researcher Award

Ushas AK

Vikram sarabhai Space Centre, India

Ushas AK is a researcher affiliated with the Vikram Sarabhai Space Centre, India, whose indexed research profile is associated with the subject area of Artificial Intelligence. The researcher is identified in Scopus by Author ID 58765503900 and has two documents recorded in the supplied profile information. This page presents a neutral academic overview of the research profile and its suitability for consideration under the Best Researcher Award.

Ushas AK
Affiliation Vikram Sarabhai Space Centre
Country India
Scopus ID 58765503900
Documents 2
Subject Area Artificial Intelligence
Event Global Academic Awards

The available information indicates an academic research profile connected with Artificial Intelligence and an institutional setting associated with space research and technology. The supplied Scopus identifier provides a basis for bibliographic verification, while the available document count provides a limited quantitative indicator of indexed research output. Additional bibliometric information, including citations and h-index, was not supplied and is therefore not inferred in this article. [1]

Abstract

This article provides an academic recognition profile for Ushas AK, affiliated with the Vikram Sarabhai Space Centre in India and associated with the research area of Artificial Intelligence. The supplied bibliographic information identifies two Scopus-indexed documents under Scopus Author ID 58765503900. The profile is considered in relation to the Best Researcher Award under the Global Academic Awards. The assessment emphasizes documented research affiliation, indexed scholarly output, subject-area relevance, and the availability of verifiable bibliographic information. [1]

Keywords

Best Researcher Award; Ushas AK; Artificial Intelligence; Vikram Sarabhai Space Centre; Scopus; research profile; scholarly publications; bibliometric assessment; Global Academic Awards; academic research.

Introduction

Academic recognition generally considers a combination of scholarly productivity, research relevance, documented contributions, and evidence of research impact. Bibliographic databases such as Scopus provide structured records that can assist in evaluating an author’s indexed research output and identifying publications associated with a researcher identifier. [1]

In the supplied profile, Ushas AK is associated with the Vikram Sarabhai Space Centre and the subject area of Artificial Intelligence. These details establish a clear research context for evaluating the profile, although a complete award assessment would ordinarily require additional evidence such as publication details, citation counts, h-index, research contributions, and other supporting documentation.

Research Profile

The supplied research profile identifies Ushas AK as a researcher at the Vikram Sarabhai Space Centre in India, with Artificial Intelligence specified as the subject area. The Scopus Author ID 58765503900 provides a persistent identifier for locating the researcher’s indexed author record. [1]

Two documents are reported in the supplied information. This document count should be interpreted as a bibliographic indicator rather than a complete measure of research quality or influence. A broader evaluation may consider the nature of the publications, authorship contributions, venues, citations, methodological significance, and relevance to the researcher’s field.

Research Contributions

Based on the supplied information, the research profile is situated within Artificial Intelligence and is institutionally connected with the Vikram Sarabhai Space Centre. This combination places the profile within a research environment where computational and intelligent technologies may be relevant to advanced scientific and technological applications. However, specific research contributions cannot be attributed without publication titles, abstracts, project information, or other primary evidence.

  • Documented affiliation with the Vikram Sarabhai Space Centre.
  • Research subject area identified as Artificial Intelligence.
  • Two documents reported in the supplied Scopus profile information.
  • A persistent Scopus Author ID enabling bibliographic profile verification.

Publications

The supplied data records two documents associated with the researcher in Scopus. Individual publication titles, journal or conference information, publication dates, DOI identifiers, and citation counts were not provided in the input data. Accordingly, specific publications and DOI records are not reproduced here to avoid attributing bibliographic information that has not been verified.

The Scopus author profile is the appropriate source for reviewing the indexed document list and confirming publication-level metadata. [1]

Research Impact

Research impact can be examined through multiple indicators, including citations, h-index, publication quality, collaboration, adoption of research findings, and broader scholarly or technological influence. For this profile, the supplied input does not include a citation total or h-index. Therefore, no numerical impact score is assigned.

The presence of two indexed documents establishes a documented publication record in the supplied information, but the available data alone are insufficient to quantify citation-based impact. A fuller assessment should incorporate current bibliometric information from the researcher’s verified author record. [1]

Award Suitability

Based on the supplied information, Ushas AK appears to have a relevant academic profile for consideration for the Best Researcher Award associated with the Global Academic Awards. The principal supporting factors are the documented institutional affiliation, identification of Artificial Intelligence as the research subject area, and the presence of two documents in the supplied Scopus information. [1]

Award suitability should nevertheless be treated as a recognition assessment rather than a definitive ranking. A comprehensive review would benefit from verified information concerning citation performance, h-index, publication quality, research originality, collaboration, specific research outcomes, and broader scholarly contributions. Since these indicators were not supplied, the present assessment remains limited to the available profile evidence.

  • Relevant research specialization in Artificial Intelligence.
  • Institutional affiliation with the Vikram Sarabhai Space Centre.
  • Documented Scopus author identification.
  • Two indexed documents reported in the supplied profile information.
  • Additional bibliometric and qualitative evidence recommended for a complete award evaluation.

Conclusion

Ushas AK has a documented research profile associated with the Vikram Sarabhai Space Centre and Artificial Intelligence, with two documents reported under Scopus Author ID 58765503900. The available information provides a reasonable basis for academic recognition consideration under the Best Researcher Award, while a complete evaluation would require further evidence concerning research impact, publication quality, citations, h-index, and substantive research contributions.

Sangchun Choi | Artificial Intelligence | Best Researcher Award

Published on by Global Academic Awards

Best Researcher Award

Sangchun Choi
Soonchunhyang University
Sangchun Choi
Affiliation Soonchunhyang University
Country South Korea
Scopus ID 57213151463
Documents 35
Citations 464
h-index 11
Subject Area Artificial Intelligence
Event Global Academic Awards
ORCID Research Profile Available

Sangchun Choi of Soonchunhyang University, South Korea, whose research contributions in artificial intelligence have been reflected through peer-reviewed publications, scholarly citations, and recognized academic engagement.[1]

Abstract

Sangchun Choi has established a documented research presence within the field of artificial intelligence through scholarly publications, citation performance, and participation in internationally recognized research activities. Academic metrics indexed through Scopus indicate a consistent contribution to scientific literature, supporting recognition within the context of research excellence and academic achievement.[1]

Keywords

Artificial Intelligence, Machine Learning, Research Excellence, Scholarly Publications, Citation Impact, Academic Recognition, Best Researcher Award, Scientific Contribution.

Introduction

Research awards serve as mechanisms for acknowledging scholarly productivity, innovation, and influence across academic disciplines. Evaluation criteria commonly include publication quality, citation performance, research originality, and broader contributions to scientific advancement. Within this framework, Sangchun Choi’s documented record in artificial intelligence demonstrates characteristics frequently associated with research distinction and academic recognition.[1]

Research Profile

Sangchun Choi is affiliated with Soonchunhyang University in South Korea and maintains a research portfolio indexed in Scopus under Author ID 57213151463. The available bibliometric profile reports 35 indexed documents, 464 citations, and an h-index of 11, indicating measurable scholarly engagement and influence within the research community.[1]

  • Affiliation: Soonchunhyang University
  • Country: South Korea
  • Primary Subject Area: Artificial Intelligence
  • Indexed Documents: 35
  • Total Citations: 464
  • h-index: 11

Research Contributions

The research activities associated with Sangchun Choi contribute to ongoing developments in artificial intelligence and related computational methodologies. Published work in this area supports advancements in intelligent systems, data-driven decision-making, and emerging digital technologies. Such contributions enhance scholarly dialogue and provide foundations for future investigations.[2]

  • Development of artificial intelligence methodologies.
  • Contribution to peer-reviewed scientific literature.
  • Support for interdisciplinary computational research.
  • Participation in knowledge dissemination through scholarly publication.

Publications

The research record includes publications indexed within internationally recognized databases. Publication activity demonstrates engagement with contemporary scientific challenges and contributes to cumulative knowledge development within artificial intelligence and associated domains.[1]

  1. Peer-reviewed journal articles.
  2. Conference proceedings.
  3. Collaborative research publications.
  4. Works indexed through Scopus and related scholarly databases.

Representative scholarly outputs may include publications associated with digital intelligence, machine learning applications, and advanced computational frameworks. DOI-linked research dissemination supports transparency, discoverability, and long-term accessibility within the scientific record.[3]

Research Impact

Research impact can be evaluated through citation indicators, publication visibility, and influence on subsequent investigations. With 464 citations and an h-index of 11, Sangchun Choi’s scholarly record demonstrates measurable engagement from the academic community. Such indicators suggest that published research has contributed to ongoing scientific discussions and knowledge advancement.[1]

Award Suitability

Eligibility for the Best Researcher Award may be assessed through objective academic indicators including publication volume, citation performance, research quality, innovation, and disciplinary impact. The documented scholarly achievements of Sangchun Choi align with several commonly recognized evaluation criteria utilized by academic award committees. Bibliometric performance, research continuity, and subject-area relevance collectively support consideration for research excellence recognition.[1]

Conclusion

Sangchun Choi’s academic profile reflects sustained engagement in artificial intelligence research through peer-reviewed publications, scholarly citations, and measurable research impact. The combination of documented productivity, citation performance, and contribution to scientific advancement provides a scholarly basis for recognition within the framework of the Best Researcher Award presented by the Global Academic Awards program.[1]

References

  1. Scopus author details: Sangchun Choi, Author ID 57213151463. Scopus.https://www.scopus.com/authid/detail.uri?authorId=57213151463
  2. Artificial Intelligence-Derived Electrocardiogram Analysis for Identification of Carbon Monoxide-Induced Cardiomyopathy: A Retrospective Studyhttps://www.mdpi.com/1648-9144/62/6/1081
  3. Role of Aspartate in Immune Response and Mortality in a Polymicrobial Sepsis Model: Insights from Metabolomics and Transcriptomicshttps://www.mdpi.com/2073-4409/15/6/513

Wei Huang | Anomaly Detection | Research Excellence Award

Published on by Global Academic Awards

Mr. Wei Huang | Anomaly Detection | Research Excellence Award

Mr. Wei Huang | Beijing University of Posts and Telecommunications | China

Mr. Wei Huang’s research focuses on data science, machine learning, and intelligent data analysis, particularly in financial risk prediction and model optimization. His work includes developing advanced methods such as temporal folding, spatio-temporal pseudo-label denoising, and dynamic data selection to enhance model performance and efficiency. He has contributed to research published in leading journals, including Information Processing & Management and IEEE Transactions on Knowledge and Data Engineering. His research advances scalable, efficient, and accurate data-driven decision-making systems.

View ORCID Profile

Featured Publications

Faizan Ullah | Artificial Intelligence | Research Excellence Award

Published on by Global Academic Awards

Dr. Faizan Ullah | Artificial Intelligence | Research Excellence Award

Dr. Faizan Ullah, researcher at Forschungszentrum Jülich, Germany, works in deep learning, data mining, and image processing. Research highlights include brain tumor segmentation, federated learning, IoT cybersecurity, and medical diagnostics. He holds 1,128 citations, h-index 18, and significant contributions to secure, AI-driven healthcare systems.

Citation Metrics (Google Scholar)

1200

900

600

300

0

Citations 1128

h-index
18

i10-index 23

Citations
h-index
i10-index


View Google Scholar Profile

Featured Publications

Zeeshan Rasheed | Machine Learning | Research Excellence Award

Published on by Global Academic Awards

Mr. Zeeshan Rasheed | Machine Learning | Research Excellence Award

Mir Chakar Khan Rind University Sibi | Pakistan

Mr. Zeeshan Rasheed is an academic researcher in computer science with a focus on wireless communication systems, artificial intelligence, machine learning, and IoT-enabled network optimization. His research addresses sustainable wireless resource modeling, radio network cooperation, intelligent dataflow strategies for heterogeneous IoT environments, and predictive analytics applied to healthcare and telecommunications. He has published in multidisciplinary international journals such as Data Intelligence, MDPI Smart Cities, and the African Journal of Biomedical Research, highlighting an applied and problem-oriented research approach. With 2 Scopus-indexed publications, 5 citations, and an h-index of 1, his work reflects an emerging research trajectory that integrates AI-driven models with real-world technological and societal challenges, demonstrating growing interdisciplinary research potential as an early-career researcher.

Citation Metrics (Scopus)

8

6

4

2

0

Citations 5

Documents 2

h-index 1


View Scopus Profile
   View Orcid Profile
   View Google Scholar Profile

Featured Publications

Rebwar Khalid | Artificial Intelligence | Editorial Board Member

Published on by Global Academic Awards

Dr. Rebwar Khalid | Artificial Intelligence | Editorial Board Member

Erbil Polytechnic University | Iraq

Dr. Rebwar Khalid Hamad is an emerging researcher in artificial intelligence with a strong focus on nature-inspired algorithms, metaheuristics, and data-driven optimization systems. His work advances cutting-edge computational models such as the Krill Herd, FOX, Gravitational Search, and GOOSE algorithms, contributing significantly to optimization theory and its real-world engineering and healthcare applications. He has developed impactful frameworks for intelligent problem-solving, integrated AI-based search techniques, and enhanced algorithmic performance through systematic reviews and novel implementations. His publications in high-impact journals highlight his ability to bridge theoretical AI mechanisms with advanced data management and practical optimization challenges. Beyond research, he contributes to academic development through teaching, student supervision, and the design of data management systems. His scholarly portfolio demonstrates strong analytical capabilities, innovation in metaheuristic modeling, and a commitment to advancing the fields of artificial intelligence, data science, and computational optimization.

Profile:  Google Scholar

Featured Publications

Hamad, R. K., & Rashid, T. A. (2023). GOOSE algorithm: A powerful optimization tool for real-world engineering challenges and beyond. Evolving Systems.

Hamad, R. K., & Rashid, T. A. (2023). Current studies and applications of Krill Herd and Gravitational Search Algorithms in healthcare. Artificial Intelligence Review, 56(Suppl 1), 1243–1277.

Hamad, R. K., & Rashid, T. A. (2023). A systematic study of Krill Herd and FOX algorithms. In Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB) (pp. 168–186).

Hamad, R. K., & Rashid, T. A. (2025). A systematic study of GOOSE algorithms. In Multi-objective Optimization Techniques: Variants, Hybrids, Improvements, and Applications.

Hamad, R. K. (2024). GOOSE algorithm: A powerful optimization tool for real-world engineering challenges and beyond [Computer software]. GitHub.

Lisandra Díaz de la Paz | Data Science | Best Researcher Award

Published on by Global Academic Awards

Assoc. Prof. Dr. Lisandra Díaz de la Paz | Data Science | Best Researcher Award

Assoc. Prof. Dr. Lisandra Díaz de la Paz, Central University “Marta Abreu” of Las Villas, Cuba

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a Cuban computer scientist and academic with a Ph.D. in Technical Sciences (2023), a Master’s (2011), and a Bachelor’s (2008) in Computer Science from the Central University “Marta Abreu” of Las Villas (UCLV). She serves as an Associate Professor and researcher specializing in databases, decision-support systems, data integration, metadata management, and artificial intelligence. With over 15 years of teaching experience, she has instructed various undergraduate and postgraduate courses in computer science and related fields. Dr. Díaz de la Paz has completed extensive postgraduate training in areas such as software engineering, machine learning, and data science. She currently leads the Information Systems discipline and serves as Vice Dean of Research and Postgraduate Studies at the MFC Faculty, UCLV. Her research focuses on data quality models, big data, Python programming, semantic web, LLMs, and generative AI. She is an active contributor to Cuba’s technological advancement.

Publication Profile

Orcid

Google Scholar

Academic Background

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a distinguished academic in the field of Computer Science with a robust educational foundation acquired from the Central University “Marta Abreu” of Las Villas (UCLV), Cuba. She earned her Bachelor’s degree in Computer Science in July 2008, followed by a Master’s degree in the same field in December 2011. Demonstrating a continuous commitment to academic excellence and research, she completed her Doctorate in Technical Sciences in November 2023. This progression reflects her deepening expertise and scholarly dedication within computing and technical disciplines. Her academic journey at UCLV has equipped her with strong theoretical and practical knowledge, forming the basis for her professional contributions as a university professor, researcher, and academic leader. Dr. Díaz de la Paz’s qualifications underpin her role in advancing research in artificial intelligence, databases, and data systems while mentoring the next generation of computing professionals in Cuba and beyond.

Professional Role and Academic Specialization

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a dedicated professor and researcher with a strong focus on the field of Computer Science. Currently holding the academic rank of Associate Professor, she plays a vital role in higher education by teaching, mentoring, and guiding students across multiple levels of university instruction. Her primary specialization lies in computing, where she has developed expertise in areas such as databases, data quality, artificial intelligence, decision-support systems, and big data technologies. As both an educator and researcher, she combines theoretical knowledge with practical applications, contributing to academic excellence and technological advancement. Her position as a faculty member enables her to engage in curriculum development, academic leadership, and innovative research initiatives. Dr. Díaz de la Paz’s dual role as a professor and researcher allows her to bridge the gap between knowledge creation and dissemination, making her an influential figure in the Cuban academic and scientific community.

Awards and Recognitions

Assoc. Prof. Dr. Lisandra Díaz de la Paz has received multiple prestigious awards in recognition of her contributions to computing and educational technologies. She was a co-author of the project “Algorithms and Tools for the Library Management System,” which earned the 2024 Provincial CITMA Award in Villa Clara. In 2021, she received the Provincial CITMA Award for her work on improving the accuracy and completeness of bibliographic records in MARC 21 format. In 2019, she received the Annual Award from the Minister of Higher Education for her research in database systems and computing. Her 2018 work on the ABCD Library Management System implementation across Cuban higher education institutions was recognized for its scientific and educational impact. She also received CITMA awards in 2016 and 2012 for her innovative contributions to active database rule maintenance and business rule implementation in relational databases, respectively—highlighting her sustained excellence in research and technical innovation.

Research Focus

Assoc. Prof. Dr. Lisandra Díaz de la Paz focuses her research primarily on data quality, metadata management, bibliographic systems, and decision support through data-driven computing. Her work encompasses key areas such as the completeness and accuracy of bibliographic records in MARC 21 format, ETL process optimization, metadata profiling, and author name disambiguation using ontologies and deep learning. She has also explored big data integration with NoSQL systems, MapReduce techniques for anomaly detection, and frameworks for metadata quality evaluation in the context of open science. Her contributions have practical applications in library science, digital repositories, and institutional decision-making, particularly within educational and academic information systems. Additionally, her interdisciplinary approach blends artificial intelligence, machine learning, semantic web technologies, and business intelligence, supporting national and international collaboration for improving data infrastructure. These efforts position her as a leading researcher in data-centric computing, database technologies, and intelligent information systems.

Publication Top Notes

  • 📘 Algorithm to correct instance-level anomalies in large data using MapReduce – Cited by 7 – 2016

  • 📗 Data quality analysis in ABCD suite sources – Cited by 7 – 2015

  • 📕 Techniques to capture changes and maintain updated data warehouse – Cited by 5 – 2015

  • 📙 Data market for decision-making on teaching/research staff at UCLV – Cited by 5 – 2013

  • 📒 Techniques to capture data changes (extended version) – Cited by 4 – 2015

  • 📓 Automation of data loading processes in HR data market at UCLV – Cited by 4 – 2014

  • 📘 Weights estimation in completeness measurement of bibliographic metadata – Cited by 3 – 2021

  • 🧠 Author name disambiguation using ontology & deep learning – Cited by 1 – 2022

  • 📊 CompMARC tool for measuring completeness in MARC 21 – Cited by 1 – 2016

  • 📚 Model for metadata quality evaluation: Proposal for open science – Published – 2024

  • 📝 Accuracy measurement of author names in MARC 21 records – Published – 2018

  • 📈 Optimal weight estimation for completeness in MARC 21 metadata – Published – 2017

  • 🔍 Metadata profiling tool in MARC 21 PMMarc v2.0 – Published – 2017

  • 💾 Method for selecting data model and NoSQL system in big data – Published – 2017

  • 🛠 Procedure to improve completeness in MARC 21 records – Published – 2017

Shunzhi Yang | Artificial Intelligence | Best Researcher Award

Published on by Global Academic Awards

Mr. Shunzhi Yang | Artificial Intelligence | Best Researcher Award

Mr. ShunzhiYang at Shenzhen Polytechnic University, China

Shunzhi Yang , born on November 7, 1994, is a Doctor of Engineering specializing in Artificial Intelligence and Computer Vision. He is currently a researcher and faculty member at the School of Artificial Intelligence, Shenzhen Polytechnic University. With a strong academic background and extensive research experience, he has contributed significantly to knowledge distillation, deep learning, and object recognition. His work has been published in top-tier journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence. Yang’s dedication to AI innovation and education has established him as a prominent figure in the field.

Publication Profile

Google Scholar

Academic Background

Shunzhi Yang began his academic journey at Shenzhen Polytechnic University, earning a Junior College degree in Medical Electronics Engineering (2012–2015). He then pursued a Bachelor’s degree in Computer Science and Technology at Hanshan Normal University (2015–2017). His research career deepened at South China Normal University, where he completed a Master’s in Computer Science and Technology (2017–2020) under Professor Zheng Gong. Continuing at the same institution, he obtained his PhD in Software Engineering (2020–2023), working under Professors Zhenhua Huang and Mengchu Zhou. His diverse educational background laid a strong foundation for his AI research.

Professional Background

During his PhD, Shunzhi Yang was jointly trained at the Institute of Applied Artificial Intelligence in the Guangdong-Hong Kong-Macao Greater Bay Area, working under Professor Jinfeng Yang (2022–2023). He then joined the School of Artificial Intelligence at Shenzhen Polytechnic University in September 2023, where he currently contributes to AI research and education. His professional experience bridges both academia and applied AI, focusing on student-centered knowledge distillation and deep learning advancements. His role at Shenzhen Polytechnic University allows him to mentor students while actively engaging in groundbreaking AI research.

Awards and Honors

Shunzhi Yang has been recognized for his significant contributions to AI and Computer Vision. His research papers have been published in prestigious journals like IEEE Transactions on Pattern Analysis and Machine Intelligence. His work on feature map distillation and skill-transferring knowledge distillation has received acclaim from the research community. He has collaborated with leading AI researchers, contributing to major advancements in deep learning and neural networks. While specific awards and honors are not listed, his extensive publication record and impact in AI research position him as a highly respected academic in the field.

Research Focus

Shunzhi Yang’s research primarily focuses on Artificial Intelligence, Computer Vision, and Knowledge Distillation. His work includes student-centered learning models, adaptive temperature distillation, and lightweight deep learning architectures for low-resolution object recognition. He is particularly interested in improving AI efficiency for real-world applications, such as edge computing and neural network optimization. His research spans top-tier journals, covering essential advancements in AI-based knowledge transfer and model compression. His contributions help make AI systems more effective, efficient, and applicable to various domains, including education and autonomous systems.

Publication Top Notes

Making accurate object detection at the edge: Review and new approach

📅 2022 |  Cited by: 79 |  Artificial Intelligence Review 55 (3), 2245-2274

EdgeRNN: A compact speech recognition network with spatio-temporal features for edge computing

📅 2020 |  Cited by: 68 | IEEE Access 8, 81468-81478

Feature map distillation of thin nets for low-resolution object recognition

📅 2022 | Cited by: 66 | IEEE Transactions on Image Processing 31, 1364-1379

EdgeCRNN: An edge-computing oriented model of acoustic feature enhancement for keyword spotting

📅 2022 | Cited by: 24 | Journal of Ambient Intelligence and Humanized Computing, 1-11

Conclusion

Shunzhi Yang is a highly qualified candidate for the Best Researcher Award, given his strong academic background, extensive research contributions, and impactful publications in top-tier AI journals. Holding a PhD in Software Engineering, he has specialized in Artificial Intelligence and Computer Vision, collaborating with distinguished professors and contributing to key advancements in knowledge distillation, skill transfer learning, and deep learning optimization. His work has been recognized in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, showcasing his influence in the AI research community. Additionally, his experience in applied AI research within the Guangdong-Hong Kong-Macao Greater Bay Area and his role in AI education at Shenzhen Polytechnic University further reinforce his eligibility. If the award primarily values high-impact research, influential publications, and AI advancements, Yang stands as a strong contender; however, factors like patents, real-world implementations, and leadership roles may require further evaluation for a holistic comparison with other candidates.

 

 

Abba Bashir | Machine Learning | Best Researcher Award

Published on by Global Academic Awards

Mr. Abba Bashir | Machine Learning | Best Researcher Award

Mr. Abba Bashir, Federal University Dutsin-ma, Nigeria

Abba Bashir is a civil engineer and academic dedicated to sustainable infrastructure and structural optimization. He is a lecturer at the Federal University Dutsin-ma (FUDMA), Katsina, Nigeria, specializing in structural engineering and artificial intelligence applications in construction. With over 100 citations and an h-index of 6, his research focuses on recyclability, fiber-reinforced concrete, and computational mechanics. He has authored a book on bamboo fiber-reinforced concrete and actively contributes to accreditation and curriculum development. As the AI Research Leader at FUDMA’s Faculty of Engineering, he integrates machine learning into structural design for sustainable and resilient infrastructures.

Publication Profile

Scopus

Orcid

Google Scholar

🎓 Education

Abba Bashir is currently pursuing a Master of Technology in Structural Engineering at Mewar University, India (2023–2025). He holds a Bachelor of Technology in Civil Engineering from Sharda University, India, graduating in 2017 with an 8.3/10 CGPA. His early education includes a Senior Secondary School Certificate from Nasara Academy, Kano, Nigeria (2007) and a Primary School Leaving Certificate from Maitasa Special Primary School, Kano, Nigeria (2001). His academic journey has equipped him with expertise in structural analysis, computational mechanics, and sustainable construction materials. His continuous pursuit of knowledge fuels his research in optimizing civil engineering designs through artificial intelligence and machine learning.

💼 Experience

Abba Bashir has been a lecturer at Federal University Dutsin-ma (FUDMA) since 2020, teaching courses such as Structural Analysis, Concrete Design, and Construction Materials. He has supervised undergraduate research projects and actively contributes to curriculum development and accreditation at the university. As a practicing civil engineer since 2017, he has designed and constructed residential, commercial, and institutional structures, integrating AI-driven optimization techniques. He is a member of FUDMA’s Concrete and Steel Research Group and serves as the AI Research Leader. His expertise spans finite element modeling, numerical analysis, and sustainable building materials. He is proficient in ABAQUS, ANSYS, AutoCAD, MATLAB, and Python for structural simulations.

🏆 Awards & Honors

Abba Bashir has been recognized for his contributions to structural engineering and AI-driven construction methodologies. He has received accolades for his research on bamboo fiber-reinforced concrete and his role in advancing sustainable materials. His academic leadership in AI applications within civil engineering has earned him university recognition. His book on bamboo fiber-reinforced concrete is a significant contribution to sustainable construction literature. As a mentor and research leader, he plays a crucial role in developing new undergraduate programs and fostering innovation in civil engineering education. His expertise in computational mechanics and recyclability research continues to influence the field.

🔬 Research Focus

Abba Bashir’s research integrates artificial intelligence, machine learning, and optimization algorithms into structural engineering. His work focuses on fiber-reinforced concrete, recyclability, and sustainability in construction materials. He has extensive experience in finite element modeling using ABAQUS and ANSYS, with a strong emphasis on computational mechanics. His studies explore mechanical properties and durability of cementitious materials with micro/nano reinforcements. He also investigates the optimization of structural designs to reduce environmental impact and enhance resilience. His multidisciplinary research combines AI, numerical modeling, and advanced construction materials to create sustainable and cost-effective infrastructure solutions.

 

Publication Top Notes

1️⃣ Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste | Cited by: 50 | 📅 2022

2️⃣ An overview of streamflow prediction using random forest algorithm | Cited by: 19 | 📅 2022 🌊🤖

3️⃣ Analysis of Bamboo fibre reinforced beam | Cited by: 17 | 📅 2018 🎍🏗️

4️⃣ Antioxidant, hypolipidemic and angiotensin converting enzyme inhibitory effects of flavonoid-rich fraction of Hyphaene thebaica (Doum Palm) fruits on fat-fed obese Wistar rats | Cited by: 16 | 📅 2019 🏥🧪

5️⃣ Assessment of Water Quality Changes at Two Locations of Yamuna River Using the National Sanitation Foundation of Water Quality (NSFWQI) | Cited by: 15 | 📅 2015 🚰📊

6️⃣ High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm | Cited by: 14 | 📅 2023 🏗️🤖

7️⃣ Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques | Cited by: 8 | 📅 2022 🌊🧠

8️⃣ Comparison of Properties of Coarse Aggregate Obtained from Recycled Concrete with that of Conventional Coarse Aggregates | Cited by: 5 | 📅 2018 ♻️🏗️

9️⃣ Machine Learning: A Way to Smart Environment | Cited by: 1 | 📅 2021 🤖🌱

🔟 A new strategy using intelligent hybrid learning for prediction of water binder ratio of concrete with rice husk ash as a supplementary cementitious material | 📅 2025 🏗️📊

 

 

 

 

 

Zuheng Ming | Artificial intelligence | Best Researcher Award

Published on by Global Academic Awards

Dr. Zuheng Ming | Artificial intelligence | Best Researcher Award

Associate professor at Sorbonne Paris North University, France

🧑‍🏫 Dr. Zuheng Ming is an Assistant Professor at L2TI, Sorbonne Paris North University, France. He earned his PhD in 2013 from Grenoble Alpes University 🇫🇷, specializing in speech parameter mapping. His expertise spans multimodal learning, computer vision, and deep learning 🤖. Dr. Ming has 30+ publications 📝 in top-tier journals (JCR Q1/Q2) and conferences (ICIP, ICPR, ICDAR). He has supervised doctoral and master’s theses and collaborated internationally with CVC, RIKEN AIP, and Oulu University 🌍. He has led funded research projects on face anti-spoofing and document analysis 📄. Additionally, he serves as a guest editor and reviewer for prestigious journals. ✨

Publication Profile

Google Scholar

🏅 Professional Experience

Dr. Zuheng Ming is an accomplished researcher and educator in computer vision and deep learning 🤖. Since September 2022, he has been serving as an Assistant Professor at L2TI, Sorbonne Paris North University, France 🇫🇷. Prior to this, he was a Lecture-Researcher at L3i, La Rochelle University (2021-2022) 📚. From 2016 to 2021, he worked as a Postdoctoral Fellow and Assistant Lecturer at L3i, La Rochelle University. Earlier, from 2014 to 2015, he pursued a postdoctoral fellowship at Bordeaux University 🏛️, contributing significantly to cutting-edge research in multimodal learning and artificial intelligence. ✨

🎓 Educational Background

Dr. Zuheng Ming holds a PhD in Computer Science from Grenoble Alpes University, France (2013) 🇫🇷, where he specialized in spectral parameters mapping for cued speech using multi-linear and GMM approaches 🔬. He earned his Master’s degree in Pattern Recognition and Artificial Intelligence from Beijing Institute of Technology (2008) 🎭🤖. His academic journey began with a Bachelor’s degree in Electronic and Automatic Systems Engineering from Hunan University, China (2003) ⚡. His strong educational foundation has driven his research contributions in computer vision, deep learning, and multimodal learning 📚✨.

🔬 Research Activities

Dr. Zuheng Ming has been actively involved in research supervision, mentoring 1 PhD thesis, 2 Master’s theses, and 6 internships 🎓📖. He has established six international collaborations with prestigious institutions, including CVC (Spain) 🇪🇸, RIKEN AIP (Japan) 🇯🇵, Oulu University (Finland) 🇫🇮, Northwestern Polytechnical University (China) 🇨🇳, and Xidian University (China) 🇨🇳. His global academic engagement also includes an academic visit to Kyoto University, Japan, in 2015 🌍🏫. Through his extensive research network, Dr. Ming continues to make significant contributions to computer vision, deep learning, and multimodal learning 📊🤖.

🎓 Teaching Experience

Dr. Zuheng Ming has extensive teaching experience in cutting-edge technologies related to artificial intelligence and computer vision 🧠📸. He has taught courses on Deep Learning, Advanced Image Processing, and Intelligent Systems in Computer Vision 🤖🖼️, equipping students with the latest advancements in AI. Additionally, he has imparted knowledge in Database Management and Object-Oriented Programming 💾💻, fostering strong software development skills. His expertise in both theoretical foundations and practical applications makes him a valuable mentor in the field of AI and computer vision, guiding students toward innovative research and industry-ready solutions 🚀📚.

🔍 Research Focus

Dr. Zuheng Ming’s research primarily focuses on computer vision, deep learning, and document security 🧠📸🔏. His contributions span facial recognition, anti-spoofing techniques, and face liveness detection 🤖😃, enhancing biometric security. He has also worked extensively on document image classification and authentication 📄🔍, improving identity verification systems. His expertise in multi-modal learning, pattern recognition, and deep feature fusion enables advancements in AI-driven document forensics and secure authentication 🚀🔐. Collaborating internationally, he applies machine learning and self-attention networks to solve real-world challenges in face recognition, fraud detection, and intelligent systems 🌍🔬.

Publication Top Notes

📸 A survey on anti-spoofing methods for facial recognition with RGB cameras of generic consumer devices – Z Ming, M Visani, MM Luqman, JC Burie | Journal of Imaging | 88 citations | 2020

📄 Visual and textual deep feature fusion for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE/CVF Conference on Computer Vision | 63 citations | 2020

🔍 Simple triplet loss based on intra/inter-class metric learning for face verification – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | IEEE/CVF International Conference on Computer Vision | 57 citations | 2017

😊 Facial action units intensity estimation by fusion of features with multi-kernel SVM – Z Ming, A Bugeau, JL Rouas, T Shochi | IEEE International Conference on Automatic Face and Gesture Recognition | 54 citations | 2015

🆔 MIDV-2020: A comprehensive benchmark dataset for identity document analysis – BK Bulatovich, EE Vladimirovna, TD Vyacheslavovich, SN Sergeevna, … | Computer Optics | 51 citations | 2022

🙂 Dynamic Multi-Task Learning for Face Recognition with Facial Expression – Z Ming, J Xia, MM Luqman, JC Burie, K Zhao | IEEE/CVF International Conference on Computer Vision Workshop | 40 citations | 2019

📜 VLCDoC: Vision-language contrastive pre-training model for cross-modal document classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol, OR Terrades | Pattern Recognition | 33 citations | 2023

🔐 FaceLiveNet: End-to-end networks combining face verification with interactive facial expression-based liveness detection – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | International Conference on Pattern Recognition | 30 citations | 2018

📑 Cross-modal deep networks for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE International Conference on Image Processing | 23 citations | 2020

📃 Document liveness challenge dataset (DLC-2021) – DV Polevoy, IV Sigareva, DM Ershova, VV Arlazarov, DP Nikolaev, Z Ming, … | Journal of Imaging | 21 citations | 2022

📹 ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection – Z Ming, Z Yu, M Al-Ghadi, M Visani, M Muzzamil Luqman, JC Burie | IEEE International Conference on Image Processing | 21 citations | 2022

🌲 Multiple sources data fusion via deep forest – J Xia, Z Ming, A Iwasaki | IGARSS IEEE International Geoscience and Remote Sensing Symposium | 15 citations | 2018

🆔 Face detection in camera captured images of identity documents under challenging conditions – S Bakkali, MM Luqman, Z Ming, JC Burie | International Conference on Document Analysis and Recognition Workshops | 11 citations | 2019

📑 EAML: Ensemble self-attention-based mutual learning network for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | International Journal on Document Analysis and Recognition | 10 citations | 2021

🧠 Synthetic evidential study as augmented collective thought process – Preliminary report – T Nishida, M Abe, T Ookaki, D Lala, S Thovuttikul, H Song, Y Mohammad, … | ACIIDS Asian Conference | 10 citations | 2015

🆔 Identity documents authentication based on forgery detection of guilloche pattern – M Al-Ghadi, Z Ming, P Gomez-Krämer, JC Burie | arXiv preprint | 8 citations | 2022